For example, as the PT kernel seems more suitable for the processing of dependency information, it would be interesting to apply it in an architecture using these kinds of syntactic parse trees (e. g., Chen and Rambow 2003).

the combination of different extraction functions on different syntactic views may lead to very good results.


These labels can only be interpreted in the context of a specific predicate.

these argument labels are not meaningful without knowing what the predicate is.


Generally, the efficacy of the pruning algorithm correlates with the quality of syntactic parses that the semantic role labeling system takes as input.

it works much better with treebank parses than with automatic parses.


By definition, it does not vary with the constituents in a parse tree.

all constituents in the parse tree share the same subcat feature.


If there is no constituent in the parse tree that shares the same text span with an argument in the manual annotation, the system cannot possibly get a correct annotation.

the best the system can do is to correctly label all arguments that have a constituent with the same text span as in the parse tree.


Our parser is trained on the training and development set plus 275K words of broadcast news that have been recently annotated as part of the Chinese Treebank Project.

8 in addition to the training data for the semantic role labeling experiments, it also uses a portion of the treebank which has not yet been propbanked.


The second group of features are adapted from features used in the semantic role labeling of verbs.

the path feature is redefined in the semantic role labeling of nominalized predicates.


Several features that we used for the semantic role labeling of verbal predicates were dropped from our experiments with nominalized predicates.

the subcat feature and subcat+ features were not used because it is not clear how these features can be defined for a nominalized predicate.


(2004) and Moldovan and Badulescu (2005) extended the domain of linguistic analysis to that of noun phrases.

they focused on the study of four nominal constructions: complex nominals in which a head noun is modified by other nouns or adjectives derived from nouns, genitives, adjective phrases, and adjective clauses.


Our study of shallow and full syntactic information-based SRL systems was done by comparing their impact at each stage of the process.

our goal is to investigate at what stage full parsing information is most helpful relative to a shallow parsingbased system.


Our explanation is that chaining multiple processing stages to produce the final SRL analysis is crucial to understanding this analysis.

the quality of the information passed from one stage to the other is a decisive issue, and it is not necessarily judged simply by considering the F-measure.


Adhering to the most common architecture for SRL systems, our SRL system consists of four stages: pruning, argument identification, argument classification, and inference.

the goal of pruning and argument identification is to identify argument candidates for a given verb predicate.


If we assume that the classifiers return a score score(S i = c i ) that corresponds to the likelihood of argument S i being labeled c i then, given a sentence, the unaltered inference task is solved by maximizing the overall score of the arguments, In the presence of global constraints derived from linguistic information and structural considerations, our system seeks to output a legitimate labeling that maximizes this score.

it can be thought of as if the solution space is limited through the use of a filter function, F, which eliminates many argument labelings from consideration.


If there is an R-arg argument, then there has to be an arg argument.

if an argument is a reference to some other argument arg, then this referenced argument must exist in the sentence.


An integer linear program is a linear program with integral variables.

the cost function and the (in)equality constraints are all linear in terms of the variables.


This is done by setting u to be a set of indicator variables that correspond to the labels assigned to arguments.

let u ic = [S i = c] be the indicator variable that represents whether or not the argument type c is assigned to S i , and let p ic = score(S i = c).


, S j k cover the same word in a sentence, then this constraint ensures that at most one of the arguments is assigned to an argument type.

at least k − 1 arguments will be the special class null.


Similar to the argument classification stage, the only difference between full parsing-and shallow parsing-based systems is in the construction of some features.

phrase type, head word, POS tag of the head word, path, and subcategorization features are approximated using chunks and clauses when the binary classifier is trained using shallow parsing information.


In terms of filtering efficiency, around 25% of the examples are predicted as positive.

both argument identifiers filter out around 75% of the argument candidates after pruning.


Because the recall in the argument identification stage sets the upper-bound the recall in argument classification, the threshold that determines when examples are predicted to be positive is usually lowered to allow more positive predictions.

a candidate is predicted as positive when its probability estimation is larger than the threshold.


Both classifiers use very similar features to those used by the argument identifier as explained in Section 3.2, treating the target word as a constituent.

the features are predicate, POS tag of the predicate, voice, context words, POS tags of the context words, chunk pattern, clause relative position, and shallow-path.


After we carefully examined the output of the word-based classifier, we realized that it filters out easy candidates, and leaves examples that are difficult to the later stages.

these argument candidates often overlap and differ only in one or two words.


Formally speaking, the argument classifiers attempt to assign labels to a set of arguments, S 1:M , indexed from 1 to M. Each argument S i can take any label from a set of argument labels, P, and the indexed set of arguments can take a set of labels, c 1:M ∈ P M .

if we assume that the argument classifier returns an estimated conditional probability distribution, Prob(S i = c i ), then, given a sentence, the inference procedure seeks a global assignment that maximizes the objective function denoted by Equation 2, which can be rewritten as follows, where the linguistic and structural constraints are represented by the filter F. this objective function reflects the expected number of correct argument predictions, subject to the constraints.


We assign the same prior distribution to each phantom candidate.

the probability of the "null" class is set to be 0.55 based on empirical tests, and the probabilities of the remaining classes are set based on their occurrence frequencies in the training data.


First, it allows much richer syntactic information.

full parse trees generated using Collins's parser (Collins 1999) and Charniak's parser (Charniak 2001) were provided.


The main difference was that the joint-inference stage was extended to combine six basic SRL systems instead of two.

for this implementation, we first trained two SRL systems that use Collins's parser and Charniak's parser, respectively, because of their noticeably different outputs.


We performed a detailed and fair experimental comparison between shallow and full parsing information and concluded that, indeed, full syntactic information can improve the performance of an SRL system.

we have shown that this information is most crucial in the pruning stage of the system, and relatively less important in the following stages.


Among  the most obvious sources are a greater diversity in the range of use of predicates and headwords in the Brown domain.

the lexical features may be more varied in terms of predicate senses and raw number of predicates.


Although the graphs we show are for a specific simulation, the general pattern we describe is robust.

using α in place of κ does not markedly change the results; neither does increasing or decreasing the data set size.


The main idea behind the model is to recast anaphora resolution as a preference classification problem.

the model learns a classifier that determines the preference between competing candidates, and, during resolution, chooses the antecedent of a given anaphor based on the ranking of the candidates.


1 For training, instances are created for each anaphor occurring in an annotated text.

given an anaphor ana and its antecedent candidates, a set of negative instances (labeled "0") is formed by pairing ana and each of the candidates that is not coreferential with ana.


In the scheme, candidates are compared linearly from the beginning to the end.

the first candidate is compared with the second one, forming a test instance, which is then passed to the classifier to determine the preference.


Suppose we have a "perfect" classifier that can correctly determine the preference between candidates.

the candidates that are coreferential with the anaphor will be classified as preferred over those that are not.


If we define the score of C i as: Then, the most preferred candidate is the candidate that has the maximum score.

if we simply use 1 to denote the result that C i is classified as preferred over C j , and −1 if C j is preferred otherwise, then: the score of a candidate is the number of the opponents to which it is preferred, less the number of the opponents to which it is less preferred.


For training, we create "0" and "1" training instances for each encountered anaphor, that is, the NP that is coreferential with at least one preceding NP.

given an anaphor and its antecedent candidates, a positive instance is generated for the closest antecedent and a set of negative instances is generated for each of the candidates that is not coreferential with the anaphor.


we will update the scores of the two candidates in a match if and only if the preference confidence returned by the classifier is higher than a pre-specified threshold.


For better comparison with the twin-candidate model, we built another single-candidate-based system in which the non-anaphors were also incorporated for training.

for each encountered non-anaphor during training, we created a set of "0" instances by pairing the non-anaphor with each of the candidates.


Overall Coreference Resolution Having demonstrated the performance of the twin-candidate model on coreference resolution for non-pronouns, we now further examine overall coreference resolution for whole NPs, combining both pronoun resolution and non-pronoun resolution.

given an input test document, we check each encountered NP from beginning to end.


Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language.

we show that employing sublexical units called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy.


Rules of the form q λ → w q () are -rules, or state-change rules, which substitute state q for state q without producing output, and stay at the current input subtree.

multiple initial states are not needed: we can use a single start state Q i , and instead of each initial state q with starting weight w add the rule Q i True → w q () (where We define the binary derivation relation for xT transducer m on partially transformed terms and derivation histories b is derived from a by application of a rule q λ → w rhs to an unprocessed input subtree a ↓ i which is in state q, replacing it by output given by rhs with variables (q , i ) replaced by the input subtree at relative path i in state q .


This new transducer produces derivation trees on its output instead of normal output trees.

the original rhs of rules are flattened into a tree of depth 1, with the root labeled by the original rule, and all the non-expanding ∆-labeled nodes of the rhs removed, so that the remaining children are the nonterminal yield in left to right order.


One of our goals in this work is to demonstrate the contribution of limited linguistic knowledge to a machine-learning approach to an NLP task.

we used the following resources for Hebrew and Arabic: r Knowledge of word-formation processes, and in particular the behavior of the weak roots in certain paradigms (see Section 5.4) It is important to note that these resources do not constitute a method for identifying, even approximately, the root of a given word.


The experiments discussed previously are completely devoid of linguistic knowledge.

experiment B inherently assumes that any sequence of three consonants can be the root of a given word.


For each value a i (1 ≤ i ≤ k) predicted by the R 1 classifier, we run the R 2 classifier where the value of the feature for R 1 is a i .

we run the R 2 classifier k times for each word w (k depends on w).


Using the linguistic constraints encoded in the scoring function only to eliminate roots, while maintaining the ranking proposed by SNoW, yields much lower accuracy.

when we use only the list of roots as the single constraint when combining the three classifiers, thereby implementing only a filter of infeasible results, we obtain a precision of 65.24%, recall of 73.87%, and an F-score of 69.29%.


In this section we show that applying global constraints, in the form of the scoring function, not only improves global decisions but also significantly improves the local classification task.

we show that the performance of identifying each radical in isolation improves after the scoring function is applied.


As is well known from other learning tasks, fine-tuning of the feature set can produce additional accuracy; we expect this to be the case in this task, too.

introducing features that capture contextual information is likely to improve the results.


As the author promises, the book is self-contained and quite accessible to those who have little background in machine learning.

of the 12 chapters in the book, three are devoted to preparatory material, including: a brief introduction to machine learning, basic unconstrained and constrained optimization techniques (e.g., gradient descent and the method of Lagrange multipliers), and relevant linear-algebra concepts (e.g., eigenvalues, eigenvectors, matrix and vector norms, diagonalization).


One prominent representation of the set of possible system actions is the DATE scheme .

this representation is used in the COMMUNICATOR corpus annotation (Walker, Passonneau, and Boland 2001), discussed herein.


Having a large number of states is a more realistic scenario for practical, flexible, and generic dialogue systems, but it also makes many RL approaches intractable.

with a large number of states it is not possible to learn estimates of the expected future reward for every state, unless we can exploit commonalities between different states.


By adjusting the value of the unobserved state penalty u, we can adjust the extent to which this model follows the supervised policy defined by S data (s, a) or the reinforcement learning policy defined by Q data (s, a).

if u is very low, then maximizing Q hybrid (s, a) is equivalent to maximizing S data (s, a).


The user simulation is trained on exactly the same user actions that are used to evaluate the COMMUNICATOR systems, so the user simulations mimic exactly these users.

the simulation is able to mimic the effects of speech recognition errors, because it is just as likely as the real users to disagree with a confirmation or provide a new value for a previously filled slot.


These results show a clear advantage for the hybrid policy over the average across the COMMUNI-CATOR systems, as well as over each individual COMMUNICATOR system.


The main reason for introducing this framework is to allow us to characterize algorithms that have previously been described in different traditions and to compare their formal properties within a single unified framework.

whereas this type of framework has previously been used to characterize algorithms in the stackbased family (Nivre 2003(Nivre , 2006bAttardi 2006), it is here being used also for the listbased algorithms first discussed by Covington (2001).


However, in the formal characterization of different parsing algorithms in Sections 4 and 5, we will concentrate on properties of the underlying transition systems.

assuming that both o(c) and t(c) can be performed in constant time (for every o, t and c), which is reasonable in most cases, the worst-case time complexity of a deterministic parser based on a transition system S is given by an upper bound on the length of transition sequences in S. And the space complexity is given by an upper bound on the size of a configuration c ∈ C, because only one configuration needs to be stored at any given time in a deterministic parser.


This result holds on average over all languages and for five out of thirteen of the individual languages and shows that the advantage of linear-time parsing complexity (for the stack-based parsers) can be outweighed by the disadvantage of a more complex classification problem in pseudo-projective parsing.

the larger constant associated with a larger cohort of SVM classifiers for the pseudo-projective parser can be more important than the better asymptotic complexity of the linear-time algorithm in the range of sentence lengths typically found in natural language.


Although the non-projective, list-based algorithm can be said to give the best tradeoff between accuracy and efficiency when results are averaged over all languages in the sample, we have also observed important language-specific effects.

the arc-eager strategy inherent not only in the arc-eager, stack-based algorithm but also in both versions of the list-based algorithm appears to be suboptimal for some languages and syntactic representations.


First of all, as Scott (1955, page 322) points out, "[percentage agreement] is biased in favor of dimensions with a small number of categories."

given two coding schemes for the same phenomenon, the one with fewer categories will result in higher percentage agreement just by chance.


4 Expected agreement is then the probability of c 1 and c 2 agreeing on any category, that is, the sum of the product over all categories: The difference between S, π, and κ lies in the assumptions leading to the calculation of P(k|c i ), the chance that coder c i will assign an arbitrary item to category k (Zwick 1988;Hsu and Field 2003).

this coefficient is based on the assumption that if coders were operating by chance alone, we would get a uniform distribution: for any two coders c m , c n and any two categories k j , k l , P(k j |c m ) = P(k l |c n ).


The greater the number of coders, the lower the annotator bias B, and hence the lower the difference between κ and π, because the variance ofP(k|c) does not increase in proportion to the number of coders.

provided enough coders are used, it should not matter whether a single-distribution or individual-distribution coefficient is used.


In this article we present a resource that could potentially be used to address these problems.

we create a monolingual parallel corpus with human paraphrase annotations.


To take this into account, we also evaluate these measures over larger blocks (similar to Ayan and Dorr [2006]).

we extract phrase pairs from the alignments produced by our annotators using a modified version of the standard SMT phrase extraction heuristic (Och, Tillmann, and Ney 1999).


For the sake of flexibility we estimate π 0 using Monte Carlo sampling.

we approximate the full sum by drawing samples from a prior distribution over sets of phrase pairs for each of our annotators (Pr(A p atom ) and Pr(B p atom ) in Equation (6)).


Tables 3 and 4 display agreement statistics on our three corpora using precision, recall, F1, andĈ.

we estimateĈ by aggregatingπ andπ 0 into corpuslevel estimates.


In Figure 4 we show howĈ varies with sentence length for our three corpora.

we plot observed agreementπ, chance agreement π 0 , andĈ against sentence pairs Agreement statistics plotted against sentence length for the three sub-corpora.


This is not to say that going electronic-only makes the journal free of production costs.

although the journal's editor, editorial board, and external reviewers provide their labor free of charge, it still costs real money to produce the journal: CL will continue to use the professional services of the MIT Press in managing the high standards of copyediting and typesetting to which our readers have become accustomed.


First, we show how fully fledged statistical MT (SMT) systems can be extended to handle IPMT.

the TT2 systems always produce complete sentence hypotheses on which the human translator can work.


Again, decision theory tells us to maximize the probability of the suffix given the available information.

the best suffix will bet which can be straightforwardly rewritten aŝ Note that, because t p t s = t, this equation is very similar to Equation (1).


However, it should be mentioned that the whole AT approach (and similar PBM approaches as they are now called) is independent of the word clustering concept.

for large training corpora, omitting the word clustering in the AT system does not much affect the translation accuracy.


The simplicity of finite-state models has given rise to some concerns about their applicability to real tasks.

in the field of language translation, it is often argued that natural languages are so complex that these simple models are never able to cope with the required source-target mappings.


During the process of human-machine interaction the system makes use of this word graph in order to complete the prefixes accepted by the human translator.

after the human translator has accepted a prefix string, the system finds the best path in the word graph associated with this prefix string so that it is able to complete the target sentence.


We computed a CF list for each fact in each ordering by applying the instantiation of centering introduced by Kibble andPower (2000, 2004) for concept-totext generation.

we took each database fact to correspond to an "utterance" and specified the "realization" parameter using the arguments of each fact as the members of the corresponding CF list.


When comparing several metrics with each other, the one with the lowest classification error rate is the most appropriate for ordering the sentences that the OSO consists of.

the smaller the classification error rate, the better a metric is expected to perform for information ordering.


This in turn means that M.NOCB returns a better classification error rate for significantly more texts in MPIRO-CF than M.CHEAP.

m.NOCB outperforms m.CHEAP significantly in this corpus.


In this article, we look into the usefulness of some of the identified linguistic properties of idioms for their automatic recognition.

we develop statistical measures that each model a specific property of idiomatic expressions by looking at their actual usage patterns in text.


It is hard to find a single agreed-upon definition that covers all members of this class (Glucksberg 1993;Cacciari 1993;Nunberg, Sag, and Wasow 1994), but they are often defined as sequences of words involving some degree of semantic idiosyncrasy or non-compositionality.

an idiom has a different meaning from the simple composition of the meaning of its component words.


Our goal is to develop unsupervised means for the automatic acquisition of lexical, syntactic, and semantic knowledge about a broadly documented class of idiomatic expressions.

we focus on a cross-linguistically prominent class of phrasal idioms which are commonly and productively formed from the combination of a frequent verb and a noun in its direct object position (Cowie, Mackin, and McCaig 1983;Nunberg, Sag, and Wasow 1994;Fellbaum 2002), for example, shoot the breeze, make a face, and push one's luck.


Generally, the more semantically analyzable an idiom is, the easier it is to map the idiom constituents onto their corresponding idiomatic referents.

the more semantically analyzable an idiom is, the easier it is to make predictions about the idiomatic meaning from the meaning of the idiom parts.


We thus interpret this property statistically in the following way: We expect a lexically fixed verb+noun combination to appear much more frequently than its variants in general.

we examine the strength of association between the verb and the noun constituent of a combination (the target expression or its lexical variants) as an indirect cue to its idiomaticity, an approach inspired by Lin (1999).


These examples show that syntactic fixedness is not a sufficient condition for idiomaticity.

it is possible for non-idiomatic expressions to be syntactically fixed for reasons other than semantic idiosyncrasy.


We thus devise an unsupervised method that discovers the canonical form(s) of a given idiomatic verb-noun pair by examining its frequency of occurrence in each syntactic pattern under consideration.

the set of the canonical form(s) of the target pair v, n is defined as Here, P is the set of patterns (see Table 1), and the condition z(v, n, pt k ) > T z determines whether the frequency of the target pair v, n in pt k is substantially higher than its frequency in other patterns; z(v, n, pt k ) is calculated using the statistic z-score as in Equation 8, and T z is a predefined threshold.


We evaluate our unsupervised canonical form identification method by verifying its predicted forms against ODCIE and CCID.

for each of the 100 idiomatic pairs in TEST all , we calculate the precision and recall of its predicted canonical forms (those whose z-scores are above T z ), compared to the canonical forms listed in the two dictionaries.


Other forms of the expression, such as pull the weights, typically are only used with a literal meaning.

an idiom tends to have one (or a small number of) canonical form(s), which are its most preferred syntactic patterns.


The CONTEXT method labels usages of an expression in a leave-one-out strategy, where each test token is labelled by using the other tokens as noisy training (goldstandard) data.

to provide gold-standard data for each instance of an expression, we first divide the other instances (of the same expression) into likelyidiomatic and likely-literal groups, where the former group contains usages in canonical form(s) and the latter contains usages in non-canonical form(s).


In both cases, it was found that 100% recall was extremely hard to achieve due to the power-law distribution of VPC token frequencies in corpus data.

around half of the VPC lexical items found in pre-existing lexical resources occurred in the BNC at most 2 times.


Kordoni (2003bKordoni ( , 2003aKordoni ( , 2006 focused instead on the role of prepositions in diathesis alternations, and proposed an MRS analysis of indirect prepositional arguments based on English, German, and Modern Greek data.

she showed that a robust formal semantic framework like MRS provides an appropriate theoretical basis for a linguistically motivated account of indirect prepositional arguments, and also the necessary formal generalizations for the analysis of such arguments in a multilingual context (as a result of MRS structures being easily comparable across languages).


Prepositions have tended to be overlooked in NLP applications, but there have been isolated examples of prepositions being shown to be worthy of dedicated treatment.

applications requiring some level of syntactic abstraction tend to benefit from the inclusion of prepositions.


The purpose of this article has been to highlight the theoretical and applied research that has been done on prepositions in computational linguistics, focusing on computational syntax and semantics.

we have aimed to highlight applied research which has focused specifically on prepositions.


In addition to the semantic role annotations from PTB and FrameNet, traditional knowledge bases (KBs) are utilized to provide training data for the relation classification.

the Factotum KB (Cassidy 2000) is used to provide additional training data for prepositions that are used to convey particular relationships.


This article focuses on the aspects of this method relevant to the processing of prepositions.

here we specifically address preposition disambiguation using semantic role annotations from PTB, FrameNet, and Factotum.


The representation of natural language utterances often incorporates the notion of semantic roles, which are analogous to the slots in a frame-based representation.

there is an emphasis on the analysis of thematic roles, which serve to tie the grammatical constituents of a sentence to the underlying semantic representation.


The words exceeding a certain threshold are collected into a list associated with the class value, making this a "bag of words" approach.

in the experiments discussed below, a potential collocate (coll) is selected whenever the conditional probability for the class (C) value exceeds the prior probability by a factor greater than 20%: 6 for a given potential collocation word (coll) to be treated as one of the actual collocation words, the relative percent change of the class conditional probability (P(C|coll)) versus the prior probability for the class value (P(C)) must be 20% or higher.


They use a different type of collocation feature than ours: having a binary feature for each potential collocation rather than a single feature per class.

they use Over-Range Binary rather than Per-Class Binary (Wiebe, McKeever, and Bruce 1998).


The Europarl data were assembled by combining four of the bilingual sentencealigned corpora made public as part of the freely available Europarl corpus.

the Spanish-English, Italian-English, French-English and Portuguese-English corpora were automatically aligned based on exact matches of English translations.


Members are parts, but may not play any functional role with respect to their whole.

compared with Component-Integral instances such as the knob of the door, where the knob is a round handle one turns in order to open a door, in an example like bunch of cats, the cats don't play any functional role to the whole bunch.


The article describes a pilot implementation of a grammar containing different types of locative PPs.

we investigate the distinction between static and directional locatives, and between different types of directional locatives.


Lexical entries are encoded as Lexical Conceptual Structures (LCSs), as exemplified by the entry for the preposition in in (Example (20)), where we find the lexeme, the part-of-speech category, the selectional restriction, and the semantics on four separate lines.

interesting in our context is the semantic analysis of static and directional locatives (Q1/Q3), and how they combine with verbs (Q2).


Some languages use pre-or postpositions, whereas other languages use locative case.

case languages like Finnish, Hungarian, and Tsez realize the two different layers as different suffixes.


Furthermore, Kracht claims that verbs may select a PP complement of a certain mode syntactically, but only interact with the LP semantically.

the verb takes a location as the semantic argument.


In addition to relations and arguments, the semantic representations in VerbMobil are decorated with additional information which is exploited in the transfer step of the translation.

the representations for PPs and the lexical entries for prepositions contain a feature ROLE as part of the argument (to be) filled by the landmark.


All the introduced variables get used, and have a natural interpretation in our analysis.

the variable connecting the two parts, l, is a location, whereas in the NorGram analysis, the connecting variable, is typed as an event, and it is not clear what kind of denotation could be given to this event.


ERG: The analysis is discussed more in Section 8.5, where we gave examples of how ERG handled the different constructions.

eRG does not distinguish between examples (t.13) and (t.14) but ascribes the same number of analyses to them.


In this section we present an abstract implementation-independent architecture for a visually situated dialog system and highlight the role played by spatial reasoning in the functioning of the system.

we describe how models of spatial prepositional semantics are important for reference resolution and generation.


The computational models of spatial prepositions we present are designed to handle reference resolution and generation in complex visual environments containing multiple objects.

these models are designed to account for the contextual influence which the presence of multiple objects has on the semantics of topological and projective prepositions.


Semantic interpretation may be easily integrated into a probabilistic time-series model if it is formulated as a type of transition, from source to destination referents of equivalent type at adjacent time steps.

while relations in an ordinary Montagovian interpretation framework (Montague 1973) may be functions from entity referents to truth value referents, all relations in the world model defined here must be transition functions from entity referents to entity referents.


From this point, the recognizer will have the entire duration of the word to compute (in parallel, in a separate thread, or on a separate server) the set of outgoing transitions that may follow this word.

the model described herein may be scaled to richer domains because it is amenable to parallelization.


Owing to the large set of features that are used for predicate identification and argument extraction, which typically result in sparseness problems for generative models, we estimate the probabilities using a classification model.

we use the Adaboost classifier (Schapire 1999) wherein a highly accurate classifier is built by combining many "weak" or "simple" base classifiers f i , each of which may only be moderately accurate.


The importance of neutral examples has previously been noted for classifying the sentiment of documents (Koppel and Schler 2006), but ours is the first work to explore how neutral instances affect classifying the contextual polarity of words and phrases.

we found that the performance of features for distinguishing between positive and negative polarity greatly degrades when neutral instances are included in the experiments.


Subjectivity clues are words and phrases that may be used to express private states.

subjectivity clues have subjective usages, though they may have objective usages as well.


For each clue instance, the modifies/modifed by features capture whether there are adj, mod, or vmod relationships between the clue instance and any other instances from the lexicon.

the modifies strongsubj feature is true if the clue instance and its parent share an adj, mod, or vmod relationship, and if its parent is an instance of a strongsubj clue from the lexicon.


In addition, a major property of lexical collocations is their "non-substitutability", as termed in Manning and Schutze (1999).

typically neither a headword nor a modifier in the collocation can be substituted by their synonyms or other related terms.


Similarity measures that utilize MI weights showed good performance, however.

a common practice is to filter out features by minimal frequency and weight thresholds.


On the other hand, our analysis revealed that many candidate word similarity pairs suggested by distributional similarity measures do not correspond to "tight" semantic relationships.

many word pairs suggested by the LIN measure do not satisfy the lexical entailment relation, as demonstrated in Table 2.


The semantic neighborhood of w, denoted by N(w), is defined as the set of all words v which are considered sufficiently similar to w, satisfying sim(w, v) > θ sim , where θ sim is a second empirically determined threshold.

the bootstrapped feature weight, denoted weight B , is then defined by: we identify all words v that are in the semantic neighborhood of w and are also characterized by f , and then sum the values of their similarities to w. Intuitively, summing these similarity values captures simultaneously a desired balance between feature specificity and generality, addressing the observations in the beginning of this section.


The judgment criterion follows the criterion presented in Section 3.

the judges were asked to apply the two operational conditions, existence and substitutability in context, to each given pair.


An interesting behavior of the bootstrapping process is that the most prominent features for a given target word converge across the different initial similarity measures, as exemplified in Table 6.

although the initial similarity lists overlap only partly, 4 the overlap of the top 30 features for our 30-word sample was ranging between 88% and 100%.


We suggest, on the other hand, that the desired behavior of effective feature weighting is that the common features of truly similar words would be concentrated at the top ranks of both word vectors.

if the two words are semantically similar then we expect them to share their most characteristic features, which are in turn expected to appear at the higher ranks of each feature vector.


top-n is the set of top n common features to consider, where common features are sorted by the sum of their weights in the two word vectors (the same sorting as in Table 7).

acfr n (w, v) is the average rank in the two feature vectors of their top n common features.


These figures reflect a desired behavior of the bootstrapping function which concentrates most of the prominent common features for all the distributionally similar words (whether entailing or not) at the lower ranks of their vectors.

this explains the ability of our method to perform a massive feature reduction as demonstrated in Section 5, and to produce more informative vectors, while demoting and eliminating much of the noise in the original vectors.


As can be seen the acfr-ratio values are consistently higher for Bootstrapped LIN than for MI.

the bootstrapping method assigns much higher acfr n scores to entailing words than to non-entailing ones, whereas for MI the corresponding acfr n scores for entailing and non-entailing pairs are roughly equal.


That is, the bootstrapping method assigns much higher acfr n scores to entailing words than to non-entailing ones, whereas for MI the corresponding acfr n scores for entailing and non-entailing pairs are roughly equal.

we notice that the largest gaps in acfr-ratio occur for lower numbers of top common features, whose weights are indeed the most important and influential in distributional similarity measures.


She was a pillar of our Department, contributing strongly to all our academic development activities.

she ran our * This is an edited version of the eulogy delivered at Alison's funeral by Greg, with contributions from her husband Richard Tobin, and her parents David and Kay Cawsey.


So at John Cocke's suggestion we decided to model English by trigrams.

we used the approximation But this decision did not dispose of the problem.


r We introduced pronunciation modeling by triphones, thereby modeling phones as influenced by context.

in our pronunciation lexicon each word was first transformed into a string of symbols roughly corresponding to phones.


2 The problem is that the fact of being easy or hard is not directly observable, but has to be inferred from the observed annotations.

some of the observed agreements will in fact be hard instances, since coin-flips could occasionally come out all-heads or all-tails.


5 In all our calculations P(B 1 ) = ... = P(B n ), that is, a priori, any number of hard instances is equiprobable.

6 there are twice as many 3-to-2 cases than 4-to-1, corresponding to 5 3 as opposed to 5 4 .


They showed that a machine-learning classifier is sensitive to the type of noise in the data.

if the noise is in the form of category over-use (an annotator disproportionately favors a certain category), when algorithm performance is measured against the noisy data, accuracy estimates are often inflated relative to performance on the real data, uncorrupted by noise (see Figure 3(b) therein).


However, annotation noise is different from existing well-understood noise models.

random classification noise, where each instance has the same probability of having its label flipped, is known to be tolerable in supervised learning (Blum et al.


We present a semi-supervised technique for word sense disambiguation that exploits external knowledge acquired in an unsupervised manner.

we use a combination of basic kernel functions to independently estimate syntagmatic and domain similarity, building a set of word-expert classifiers that share a common domain model acquired from a large corpus of unlabeled data.


To address this problem, in recent work we presented a semi-supervised approach based on kernel methods for WSD (Strapparava, Gliozzo, and Giuliano 2004;Gliozzo, Giuliano, and Strapparava 2005;Giuliano, Gliozzo, and Strapparava 2006).

we explored the following research directions: (1) independently modeling domain and syntagmatic aspects of sense distinction to improve feature representativeness; and (2) exploiting external knowledge acquired from unlabeled data, with the purpose of drastically reducing the amount of labeled training data.


Formally, the kernel is a function K : X × X → R that takes as input two data objects (e.g., vectors, texts, or parse trees) and outputs a real number characterizing their similarity, with the property that the function is symmetric and positive semi-definite.

for all where φ is an (implicit) mapping from X to an (inner product) feature space F. Kernels are used inside learning algorithms such as support vector machines (SVM) or kernel perceptrons as the interface between the algorithm and the data.


The syntagmatic kernel is defined as a sum of gap-weighted subsequences kernels that operate at word and part-of-speech tag level.

following the approach proposed by Cancedda et al.


To solve this problem, external lexical knowledge is fed into the supervised learning process, allowing us to define the soft-matching collocation kernel.

we define two alternative soft-matching criteria by exploiting synonymy relations in WordNet and DMs acquired from corpora.


The first soft-matching criterion is based on WordNet 8 to define a similarity matrix between words.

we substitute two words if they are synonyms.


First, we evaluated our methodology on four lexical-sample tasks of Senseval-3, significantly improving the state of the art for all of them.

we demonstrated that using external knowledge inside a supervised framework is a viable methodology to reduce the amount of training data required for learning.


This situation is changing, because of the emergence of sharedtask events in NLG such as Generation Challenges (see Section 2.1.4).

we based our experiments on the SUMTIME system (Sripada et al.


SUMTIME-Hybrid uses the SUMTIME microplanner/realizer to generate text from the corpus-derived content tuples ( Figure 3).

it combines human contentdetermination with SUMTIME microplanning and realization.


In part because of this, we were less conservative in our statistical significance calculations than in the experiments reported in Section 3.2.

we computed statistical significance of correlations using one-tailed (instead of two-tailed) tests, and we did not apply a Bonferroni multiple-hypothesis correction.


Consider the following Chinese sentence and its English translation: (1) with Sharon Decoding can be cast as a (monolingual) parsing problem because we only need to parse the source-language side of the SCFG, as if we were constructing a CFG by projecting the SCFG onto its Chinese side: The only extra work we need to do for decoding is to build corresponding targetlanguage (English) subtrees in parallel.

we build synchronous trees when parsing the source-language input, as shown in Figure 1.


This scheme generalizes to the case where we have n nonterminals in a SCFG rule, and the decoder conservatively assumes nothing can be done on language model scoring (because target-language spans are non-contiguous in general) until the real nonterminal has been recognized.

target-language boundary words from each child nonterminal of the rule will be cached in all virtual nonterminals derived from this rule.


Computing treewidth of arbitrary graphs is NP-complete (Arnborg, Corneil, and Proskurowski 1987), but the graphs derived from SCFG permutations have a restricted structure that it might be possible to exploit.

the graphs have degree no greater than six.


This article presents an investigation of corpus-based methods for the automation of help-desk e-mail responses.

we investigate this problem along two operational dimensions: (1) information-gathering technique, and (2) granularity of the information.


Observations from this corpus have led us to consider several methods that implement different types of corpus-based strategies.

we have investigated two types of methods (retrieval and prediction) applied at two levels of granularity (document and sentence).


This is done by decrementing the score of a sentence that belongs to an SC for which there is a higher or equal scoring sentence (if there are several highest-scoring sentences, we retain one sentence as a reference sentence-i.e., its score is not decremented).

given a sentence s k in cluster SC l which contains a sentence with a higher or equal score, the contribution of SC l to Score(s k ) (= Pr(SC l ) × Pr(s k |SC l )) is subtracted from Score(s k ).


Hence, throughout the course of this project, the different methods had minor implementational variations, which do not affect the overall insights of this research.

we used Decision Graphs (Oliver 1993) for Doc-Pred, and SVMs (Vapnik 1998) for Sent-Pred.


Our experimental set-up is designed to evaluate the ability of the different responsegeneration methods to address unseen request e-mails.

we want to determine the applicability of our methods to different situations, namely, whether different requests are addressed only by some methods, or whether there is a significant overlap between the methods.


in this example, but without the first sentence.

the generic portion of the response is confidently produced, and the specific portion is left out due to insufficient evidence.


These results indicate that (1) there are deeper features of data sets that must be considered in order to select a single suitable technique; or (2) the selection of a technique does not depend on features of the data set itself, but on the spread of situations in the data set.

the applicability and performance of a response-generation method depends on the specifics of the situation, and different data sets contain a different spread of situations.


r Predictive methods are suitable for situations where there are relatively few responses for many, varied requests.

the responses generalize common answers to a variety of problems.


That is, the responses generalize common answers to a variety of problems.

doc-Pred is suitable when this observation applies to complete answers, as is the case for data set no.


Because the performances of the different methods are comparable, we do this by establishing a link between confidence and performance.

our meta-level process learns to predict the performance of the different methods from their confidence levels on the basis of previous experience.


Heuristics are still needed in order to decide which response-generation method to apply to an unseen case, but they are applied only after the learning is complete (Section 6.3).

the subjective process of setting performance criteria (which should be conducted by the organization running the helpdesk) does not influence the machine learning process.


The bounded-memory right-corner HHMM described in this article can also be lexicalized in this way, but because head word dependencies are most straightforwardly defined in terms of top-down PCFG-like dependency structures, this lexicalization requires the introduction of additional formal machinery to transform PCFG probabilities into right-corner form (Schuler 2009).

rather than transforming a training set of trees and mapping them to a time series model, it is necessary to transform a consistent probabilistically weighted grammar (in some sense, an infinite set of trees) into appropriately weighted and consistent right-corner PCFG and HHMM models.


Thus we have: For example, if the supply profile contains a node car with frequency of 0.25, and the same node in the demand profile has a frequency of 0.7, then b(car) is −0.45.


For comparison, we randomly selected an equal number of filler verbs, subject to the constraint that their Levin classes do not allow a causative alternation.

(none of the classes containing a filler verb allows an alternation in which the same underlying argument appears in the Subj-Intrans slot as well as the Obj-Trans slot.)


Our goal is to find a property of individual semantic profiles that, when averaged across the profiles in a data set, indicates whether our method will be able to distinguish profiles of different classes in that data set.

we aim to learn about the overall separability of the classes in a data set by investigating the properties of individual profiles that constitute the data set.


Recall that Section 6.2.2 shows that removing the "noise" distribution from each profile improves the document classification performance of our method.

subtracting the noise distribution from a profile can make it distributionally more distinct from other profiles.


10 As shown in Figure 8, the more dispersed (less closely clustered together) a set of nodes is, the further away their common ancestor is.

a highly related (and spatially constrained) set of concept nodes can be generalized to a more specific ancestor concept (i.e., near the descendants, as in Figure 8(b)), whereas a semantically distant set of concepts will be generalized to a semantically general ancestor concept (i.e., far from the descendants, as in Figure 8(a)).


Higher density values indicate a profile whose weighted concepts form more coherent clusters in the ontology.

then, we expect higher density values for the data sets from our verb alternation detection and name disambiguation tasks (on which our method had better performance than distributional methods), and lower density values for the document classification data set (on which our method had worse performance than a purely distributional method).


In the case of high dimensionality and data sparseness, words are grouped into a smaller number of (unnamed) concepts using some matrix factorization technique (e.g., SVD) or some clustering method (Pereira, Tishby, and Lee 1993).

words are grouped together based on their distributional properties instead of their explicit semantic/ontological properties.


A state of understanding each other, on the other hand, does not imply such compositionality: It remains unchanged throughout the whole period when it is true.

the meaning of events exhibits a dynamic component, whereas that of states does not.


There are two criteria for the selection of features for both representations: (Criterion 1) a clause should "talk" about important things, such as characters or locations (Criterion 2) a clause should contain background descriptions rather then events We hypothesize that sentences which satisfy both criteria are good candidates for inclusion in indicative summaries.

a summary that consists of such sentences would familiarize the reader with important elements of the setting of the story, but would not reveal the plot.


Even though ROUGE-SU4 measurement seems to have more discriminative power, it is at least puzzling that it cannot distinguish between the model and the automatically produced summaries.

placing the rule-based coarse-grained summaries and the model ones in the same group directly contradicts the results reported in Section 6.2-that people find the model summaries far superior to this particular type of summary produced by our summarizer.


The upper bound on fine-grained accuracy given a small number of semantic classes is surprisingly high.

the best reported noun WSD accuracy (78%) is achievable if we could perfectly distinguish between five semantic classes.


But the results do indicate that a better set of semantic classes may lead to significantly better WSD accuracy.

each result in Table 6 is significantly higher than previously reported supervised or unsupervised results.


These two tasks are the focus of this article.

we draw on linguistic knowledge of how blends are formed as the basis for automatically determining the source words of a blend.


Truly novel blends-neologisms which have been recently added to the language-may have differing properties from fully established forms in a dictionary.

many of our features are based on properties of the source words, both individually and in relation to each other, that may not hold for expressions that entered the language some time ago.


This is also in line with the formulation of our features, which are designed to give relatively higher values to correct candidate pairs than incorrect candidate pairs within the candidate set for a given blend; it is not necessarily the case that the feature values for the correct candidate pair for a given blend will be higher than those for an incorrect candidate pair for another blend.

the features are designed to give values that are relative to the candidates for a particular blend.


To reduce user memory load, we need alternative strategies for sequential presentation.

we require better algorithms for: selecting the most relevant subset of options to mention, as well as the attributes that are most relevant to choosing among them; and 2. determining how to organize and express the descriptions of the selected options and attributes, in ways that are both easy to understand and memorable.


As in previous work, the overall evaluation of an option is computed as the weighted sum of its evaluation on each attribute.

if f represents the option being evaluated, N is the total number of attributes, and w i and v i are, respectively, the weight and the value for attribute i, then the evaluation v( f ) of option f is computed as follows: To create a user model for a specific user, two types of information are required.


Although it is beyond the scope of this article to compare our approach to a full-blown, machine learning-based prosody prediction model, we do present in this section an expert evaluation that shows that our approach outperforms strong baseline n-gram models.

we show that the information structural constraints in the grammar play an important role in producing target prosodic boundaries, and that these boundary choices are preferred to those made by an n-gram model in isolation.


As the figure shows, the pattern seen in Figure 17 was borne out on the whole in the stimuli with theme phrases.

the theme phrases for the APML voice had an f0 drop of 90 Hz on average from the adjective to the noun, whereas the ALL voice only had an f0 drop of 50 Hz, a significant difference (t-test, 1-tailed, t 22 = 2.60, p = 0.008).


Finally, we may observe that although the APML voice had fewer boundary corrections than did the ALL voice, the difference did not reach significance, suggesting that there is room for improvement in how boundaries are handled in the APML voice.

this result suggests that edge tones, and intermediate phrase boundaries in particular, should affect the selection of units non-locally, as theoretically their effect on the pitch contour spreads back to the last pitch accent.


In contrast to these approaches, we have emphasized the generation and synthesis of sharply distinctive theme and rheme tunes in the limited domain of a dialogue system, using a hybrid rule-based and data-driven approach.


In these papers, the readability norms are represented by means of scores and classes of readability.

given a single text, the system assigns a value corresponding to a school grade.


The results reflect this tendency, with lower values for cells closer to the diagonal.

the performance for discerning grades between pairs of the 4th and 5th grades was poor.


In this article, we explore different ways of exploiting the structure of bilingual material for syntax-based SMT.

we ask what kinds of tree structures, tree labels, and word alignments are best suited for improving end-to-end translation accuracy.


The following chapter, entitled Complex Query Likelihood Retrieval Model, is devoted to extensions of the simple query likelihood model of Chapter 3.

the author reviews document-specific smoothing methods, based on document clustering and document expansion, the use of n-gram models and Markov random fields, as well as the full Bayesian query likelihood and the translation models.


Although widely used, AER has been criticized for correlating poorly with translation quality (Ayan and Dorr 2006a;Fraser and Marcu 2007b).

lower AER scores do not necessarily lead to better translation quality.


Instead of exploring the entire search space, our linear model only searches within the union of baseline predictions, which enables our system to align large bilingual corpus at a very fast speed of 3, 000 words per second.

our system is able to annotate the FBIS corpus in about 1.5 hours.


On the other hand, E→C, intersection, refined method, and Cross-EM weight precision higher.

α has a weak effect on grow-diag-final as its F-measure always keeps above 0.8 when α is varied.


Furthermore, they also claim that their technique exploits the parallel nature of a corpus more effectively than Barzilay and McKeown's approach simply because their technique uses tree paths and not just lexical information.

they propose the following modifications to Lin and Pantel's algorithm: Extracting tree paths with aligned anchors.


In this version, every successive induction of a paraphrastic pair using the same anchor pair is weighted less than the previous one.

inducing the same paraphrase pair using an anchor pair that has already been seen only counts for 1 2 n , where n is the number of times the specific anchor pair has been seen so far.


It is assumed that a phrasal replacement will occur in the exact same position in the output sentence as that of the original phrase in the input sentence.

reorderings of phrasal units are disallowed.


The remaining papers treat the problem as one of clustering speakers, rather than utterances.

they assume that during the window over which the system operates, a particular speaker is engaging in only one conversation.


This is not a statistic to be optimized (indeed, optimization is trivial: Simply make each utterance in the source into its own conversation), but it can give us some intuition about specificity.

if one subdivides a coarse-grained annotation to make a more specific variant, the many-to-one accuracy from fine to coarse remains 1.


To do this, we perform a line search over λ until we produce a clustering whose entropy is within .25 bits of the original's, then evaluate.

we measure performance given an additional piece of supervision-the annotator's preferred specificity level.


If we can have a compression component that reduces sentences to a minimal length and still retains the most important content, then we should be able to pack more information content into a fixed size summary.

sentence compression would allow summarizers to increase the overall amount of information extracted without increasing the summary length (Lin 2003;Zajic et al.


, w n the goal is to produce a summary, S, by dropping any subset of words from D. Their system uses the discourse structure of a document and the syntactic structure of each of its sentences in order to decide which words to drop.

they extend Knight and Marcu's (2002) noisy-channel model so that it can be applied to entire documents.


Thus, the more the output violates the compression rate, the larger the penalty will be.

the term ζ min • μ − ζ max • μ acts as a soft constraint providing a means to guide the compression towards the desired rate.


Although it does not explicitly capture long distance relationships between sentences, local coherence is still an important prerequisite for maintaining global coherence.

we turn to Centering Theory (Grosz, Weinstein, and Joshi 1995) and adopt an entity-based representation of discourse.


At any point in discourse, some entities are considered more salient than others, and are expected to exhibit different properties.

although each utterance may contain several entities, it is assumed that a single entity is "centered," thereby representing the current discourse focus.


Our final discourse constraint concerns pronouns.

we force personal pronouns (whose antecedent may not always be identified) to be included in the compression.


We thus created a document-based compression corpus manually.

annotators were presented with one document at a time and asked to compress sentences sequentially by removing tokens.


Due to its discriminative nature, the model is able to use a large feature set and to optimize compression accuracy directly.

mcDonald's model has a head start against our own model which does not utilize a large parallel corpus and has only a few constraints.


Unfortunately including such a constraint in the model directly breaks the Markov property in a fairly fundamental way.

computing the normalization would require the summation of 1-to-1 or near 1-to-1 weighted matchings, which is a classic #P-complete problem.


The structures of the parse trees associated with a translation are not needed by many algorithms, however.

parameter optimization by MERT requires only the rules involved in translation.


Parameters hmax and hmin specify the maximum and minimum height at which any hierarchical translation rule can be applied in the CYK grid.

a hierarchical rule can only be applied in cell (x, y) if hmin≤ y ≤hmax.


It has been shown that MBR decoding is a very effective way of combining translation hypotheses obtained from alternative morphological decompositions of the same source data.


As part of the machinery needed for our experiments we develop WFST procedures for alignment and feature extraction so that statistics needed for system optimization can be easily obtained and represented as transducers.

we make use of a lattice-based representation of sequences of rule applications, which proves useful for minimum error rate training.


We want to further study what happens when we combine BWR with LAR.

we want to investigate to what extent the integrated linguistic knowledge (from LAR) changes phrase movement in an actual SMT system, and in what direction the change takes place.


In this article, instead of deploying NLP tools or ontologies, we apply NLP ideas to IR problems.

we take a viewpoint that looks at the problem of the word mismatch between queries and documents in Web search as a problem of translating from a source language of user queries into a target language of Web documents.


In order to achieve this, we symmetrize Viterbi alignments for source-target and target-source directions by intersection only.

given two Viterbi alignments A 1 = {(a j , j)| a j > 0} and A 2 = {(i, b i )| b i > 0}, the alignments in the intersection are defined as A = A 1 ∩ A 2 .


So although Rao is technically correct that his Types 1 and 2 do represent the logical extremes of the distribution, it is not likely that any meaningful symbol systems were ever created that had either of these properties.

it is important to remember that random is not the same thing as random and equiprobable: at least some of the discussion of Rao et al.


The problem is that for that argument to work there must at least be some weight: If conditional entropy measures of a particular form correlate more with language than they do with non-linguistic systems, if even weakly, then that might count as evidence for the conclusion.

one wants a measure that can tell one, with better than chance accuracy, that the system in question is (or is not) linguistic.


The values for the kudurru texts are different than what they calculate for the Pictish stones, but crucially they are different in the direction that, given their decision tree, suggests that kudurrus are writing.

c r ≥ 4.89 and U r ≥ 1.37, yielding classification of the system as a logographic writing system.


There is a theorem, called the Chomsky-Schützenberger Theorem, that shows self-embedding recursion to be the essential element that distinguishes context-free languages from finite-state languages.

it shows that a language is properly context-free, as opposed to finite-state, only if every context-free grammar for it contains at least one self-embedding recursive nonterminal.


For example, without some kind of restriction on the conditions and actions on the arcs, ATN grammars would be Turing complete.

any single action on an arc could be Turing complete.


This suggests that as N approaches infinity, almost all the within-dictionary words would have appeared more than once, and the vocabulary growth would mainly be the growth of hapaxes formed by random alphanumeric strings, and the HVR curve would gradually approach its horizontal asymptote 1.

lim n→∞ H V = 1 A computer simulation was designed and performed to test this hypothesis.


We alleviate this problem by associating a label with each non-terminal in the rules.

each non-terminal has a label, and the whole target structure side also has a label.


Any NT substitution with this label involved is regarded as a mismatch.

there is a penalty for inserting any floating structure during decoding.


Thus, the natural choice for labeling a mode-n fiber is to use the pair formed by the labels of the two index elements that are fixed.

each mode-n fiber of a tensor X λ is labeled with the binary tuple whose elements are the labels of the corresponding fixed index elements.


We think that these are especially important experiments, because they show how the same matrix that has been used for tasks that were entirely bound to lexical items can also be used to generalize to structures that go beyond what is directly observed in the corpus.

we model here selectional preferences (how plausible a noun is as subject/object of a verb), but our method is generalizable to many other semantic tasks that pertain to composition constraints; that is, they require measuring the goodness of fit of a word/concept as argument filler of another word/concept, including assigning semantic roles, logical metonymy, coercion (Pustejovsky 1995), and many other challenges.


As an ad interim summary, we observe that TypeDM achieves competitive results in semantic tasks involving relational similarity.

in both analogy solving and two out of three relation classification experiments, TypeDM is at least as good as our LRA implementation.


We focus here instead on a potentially more common use of L×W 1 W 2 vectors as a "feature selection and labeling" space for W 1 W 2 ×L tasks.

we go back to the qualia extraction task of Section 6.2.3.


, R n withX , a tensor with n-ranks Q n ≤ R n for all modes n. Unlike the case of SVD, there is no analytical procedure to find the best lower-rank approximation to a tensor, and Tucker decomposition algorithms search for the reduced rank tensor with the best fit (as measured by least square error) iteratively.

we use the memory-efficient MET(1) algorithm of Kolda and Sun (2008) as implemented in the Matlab Tensor Toolbox.


Because for efficiency reasons we must work with just a portion of the original tensor, we thought that the AP data set, consisting of a relatively large and balanced collection of nominal concepts, would offer a sensible starting point to extract the subset.

we extract from our best tensor TypeDM the values labeled by tuples w AP , l, w 2 where w AP is in the AP set, l is one of the 100 most common links occurring in tuples with a w AP , and w 2 is one of the 1,000 most common words occurring in tuples with a w AP and a l. The resulting (sub-)tensor, APTypeDM, has dimensionality 402 × 100 × 1, 000 with 1,318,214 non-zero entries (density: 3%).


The vector space representations which serve as a basis for computing similarity can in principle be computed from any arbitrary corpus, given that it is large enough.

this need not be the same corpus as the one on which we observe predicate-headword co-occurrences.


It introduces the concepts of characters, morphemes, and words, as well as the typical morphological processes of word formation.

the authors provide sufficient details on the nature of the compounding that plays a predominant role in Chinese word formation and appears much more prevalent than that in English.


The observation you made that the object under consideration has stripes is certainly not "useless" in this case, just because you haven't ascertained whether antelopes or elephants in the jungle also have stripes.

we now know that various types of symbol sequences, from natural sequences such as DNA and proteins to man-made systems such as music and Fortran, occupy quite different entropic ranges compared to linguistic systems (Figure 1(b); Figure 8 in Schmitt and Herzel [1997]).


For example, T i -Dep==T j -Dep means T i -Dep being the same as T j -Dep or equivalent (e.g., subj and obj in R3 2 ).

we employ R1 i to extract targets (t) using opinion words (O), R2 i to extract opinion words (o) using targets (T), R3 i to extract targets (t) using extracted targets (T i ) and R4 i to extract opinion words (o) using known opinion words (O i ).


In this work, we filter non-targets based on frequency.

the one which is less frequent in the data set is removed.


(Remote) Reference: A good solution should enable one module to refer to entities defined in another.

it should enable the designer of module M i to use an entity (e.g., a type or a feature structure) defined in M j without specifying the entity explicitly.


However, the proposed solution is ad hoc and cannot be taken seriously as a concept of modularization.

the suggestion for an agreement on the feature space undermines the essence of modular design.


Notice that in S 16 , the arc (a, F, b) is interpreted as "the appropriate value of a and F is at least b."

this value may be e. S 17 maintains Appropriateness consolidation: result.


However, several aspects of the components of this simulated environment are usually hand-crafted, and thus limit the scope of policy learning.

the optimization (or reward) function is often manually set (Paek 2006).


We also show that the resulting policy performs well for real users.

we propose a five-step procedure (see Figure 1): We start by collecting data in a WOZ experiment, as described in Section 2.


We learn with the decision tree J4.8 classifier, WEKA's implementation of the C4.5 system (Quinlan 1993), and rule induction JRIP, the WEKA implementation of RIPPER (Cohen 1995).

we learn models which predict the following wizard actions: r Presentation timing: when the "average" wizard starts the presentation phase on a turn level (binary decision).


This user simulation should generate any kind of observed user behavior in a context (as opposed to the zero frequencies for sparse data), while still generating behavior which is pragmatically plausible in this situation.

we want our user simulation to generate behavior which is complete and consistent with respect to the observed actions in the data.


We use a slightly modified version of the scheme, where we only use a subset of the suggested annotation tags, while adding another level describing the output modality, as summarized in Figure 4.

the annotation scheme describes wizard actions in terms of their communication level, which describes the linguistic target after Clark (1996).


We then compare quantitative dialogue measures by performing a paired t-test.

we compare mean values of the final rewards, number of filled and confirmed slots, dialogue length, and items presented multimodally (MM items) and items presented verbally (verbal items).


Thus, the example in Figure 1 contains an instance of a discontinuous construction through the subgraph rooted at the word hearing.

the dependency arc from hearing to on spans the words is and scheduled, which are not nodes in this subgraph.


On the other hand, it may be prone to error propagation when early incorrect decisions negatively influence the parser at later stages.

maltParser uses the projective arc-eager transition system first described in Nivre (2003), which has consequences for the form of error propagation we may expect to see because the system determines the order in which arcs must be added to the graph.


Features are defined in terms of arbitrary properties of the configuration c, including the state of the stack σ c , the input buffer β c , and the partially built dependency graph G c (represented in the configuration by the arc set A c ).

many features involve properties of the two target tokens, the token on top of the stack σ c (denoted σ 0 c ) and the first token in the input buffer β c (denoted β 0 c ), which are the two tokens that may become connected by a dependency arc through the transition out of c. The basic feature representation used for all languages in the CoNLL-X shared task included three groups of features: 5 r Part-of-speech features: Note in particular that features can be defined with respect to the partially built dependency graph G c .


Secondly, both the guided models have better precision than their base model and, for the most part, also their guide model.

mST malt outperforms mST for all dependency lengths and is comparable to malt for short arcs.


We show how to apply the graph-theoretic property of treewidth to the factorization problem, and examine the question of whether efficient algorithms exist for optimizing the parsing complexity of general parsing systems in this framework.

we show that the existence of a polynomial time algorithm for optimizing the parsing complexity of general LCFRS rules would imply an improved approximation algorithm for the wellstudied problem of treewidth of general graphs.


For a rule r having n variables be the variables appearing in antecedent A i , and V(C) be the variables appearing in the consequent.

the dependency graph representation of the rule is we have a vertex for each variable in the rule, and connect any two vertices that appear together in the same antecedent, or that appear together in the consequent.


This result is intermediate between the O(n 6 ) for binarizable SCFGs, also known as Inversion Transduction Grammars (Wu 1997), and the O(n 10 ) that we would achieve by recognizing the rule in a single deduction step.

gildea andŠtefankovič (2007) use a combinatorial argument to show that as the number of nonterminals r in an SCFg rule grows, the parsing complexity grows as Ω(n cr ) for some constant c. some very difficult permutations exist of all lengths.


In contrast, several fairly minor topics, such as Blissymbolics and the Phaistos disc, are treated very thoroughly.

there is no discussion on the bandwidth and compressibility of various scripts and almost nothing on computer-mediated communication and its effect on language.


Just as the impacts of journals and authors are measured based on citations, there is no reason why reviewers should not be assessed in terms of their impacts in guiding and encouraging computational linguistics.

the database can be used to track how many journal and conference articles a person has reviewed, and how many articles were submitted to those venues.


For computational linguistics journals and conferences, it would be straightforward, under either an open peer review system and/or with permission of authors and reviewers, to collect published reviews of several articles and have potential reviewers go through a similar exercise to that provided by the BMJ.

one could focus on practice reviews for a paper.


The basic premise behind our evaluation technique can be described as follows: The distributional spread of answers in a simple, three-way decision task should directly reflect the relative distance of words on an SO (semantic orientation) scale.

we can validate our dictionaries without forcing Turkers to use our 11-point scale (including zero), making their task significantly less onerous as well as less subject to individual bias.


This set of words is then iteratively reduced, using the admissible suffix candidates (those below a certain length exceeding a heuristic threshold of overrepresentation) in each step, as long as at least two stem candidates remain.

: There must be at least two stems in the corpus appearing with all the suffix candidates.


Such considerations have led many authors to devise compression-inspired models for exploiting skewed frequencies.

several different sets of authors have invoked Minimum Description Length (MDL) as the motivation for a given formula to compress input data into a morphologically analyzed representation.


For this reason we explore several forms of textual representation going beyond the bag of words.

we generate our features over four different representations of text: Words (W): This is the traditional IR view where the text is seen as a bag of words.


Because the Types of Material field did not include news reports, we used the Taxonomic Classifier field to recognize documents from this genre.

we considered  Karlgren and Cutting 1994).


If one is going to the expense of PoS-tagging texts, only a subset of PoS tags should be used as features in AGC in order to maintain performance across changes in the topical distribution.

our results suggest that prediction accuracy on a static topic distribution should not be the sole basis for assessing the quality of Automatic Genre Classification systems: approaches that perform well on a static topic distribution can be severely impacted by changes in topical distributions.


That being said, this book contains a good introduction to type-logical grammar and its first part would make a good textbook in an advanced course on the theory of type-logical grammar.

exercises are sprinkled throughout the book that will be illuminating to the uninitiated reader.


In Chapters 3 and 4, the core of the book, several advanced CLIR models from the recent literature are discussed.

chapter 3 focuses on the collection and processing of parallel texts and on statistical translation models for query terms.


The two kinds of information are combined to evaluate the segmentation.

the decision of whether to segment a character sequence into two adjacent subsequences or not depends on both certainty and uncertainty.


Devising an unsupervised approach is similar to clarifying how infants segment words without explicit instructions.

infants are able to learn words from various kinds of information such as familiar names (Bortfeld et al.


Therefore, the arithmetic mean of frequencies of A and B is 5.

the average frequency of character sequences of length 1 is 5.


In detail, the entropy mentioned here is denoted by H(SP1L(X)) and H(SP1R(X)), where SP1L (SP1 Left) and SP1R (SP1 Right) are two subsets of SP1.

sP1L(X) and sP1R(X) mean that the left side of X is attached with one character and the right side of X is attached with one character, respectively.


Adjustment is the phase that updates the data.

it uses the result produced by the previous Selection to update the data that will be used by the next Evaluation.


For example, when the character sequence X is selected as a word after Selection, the original frequencies of X's proper subsequences are considered as being overestimated because all of them were regarded as potential words before the Selection.

if X is selected as a word, its proper subsequences will not continue to be regarded as potential words.


Limiting the length of a character sequence is different from limiting that of a word.

eSA limits the length of the input but not that of the output.


For example, the set I consists of all possible segmentations of the character sequence ABC, namely I = {A B C, AB C, A BC, ABC}, where is a delimiter.

there are three items s 0 , s 1 , and s 2 in the sequence S. s 0 = {ABC}, s 1 = {AB C, A BC}, and s 2 = {A B C}, where the subscript is the number of delimiters contained by the table 7 the empirical formulae for the prediction (linear model).


If the law is broken, there must be boundaries.

the algorithm has three rules to determine the location of boundaries: The entropy of the location is the local maxima.


The base measure of the HPYLM is also a HPYLM.

nPYLM uses a character HPYLM as the base measure of a word HPYLM.


Therefore, we evaluate ESA on the merged corpora to compare with NPYLM.

we merge the test corpora with the corresponding training ones.


Recently, Polifroni and Walker (2008) developed several variants of the SR approach that differ in how they choose and structure attributes in the summaries presented to the user.

they differ in the way they rank attributes for presentation and the method used for clustering.


The notion of a parsing schema comes from considering parsing as a deduction process which generates intermediate results called items.

items in parsing schemata are sets of partial constituency trees taken from the set of all partial parse trees that do not violate the constraints imposed by a grammar.


This version of the parser is obtained by storing an index f rather than the full buffer β f in each configuration, and then grouping configurations that share common features, making them equivalent for the side conditions of the system: Instead of storing the full set of dependency links that the algorithm has constructed up to a given point (denoted by V), we only keep track of whether elements in the stack have been assigned a head or not; and we represent this by using a stack of pairs (l, b), where l is the position of a word in the string and b is a flag which is True if the corresponding node has been assigned a head or False if it has not: To obtain a parsing schema from this deduction system, we retain only rules pertaining to the way in which the parser joins dependency structures and builds links between them.

the Reduce step is just a mechanism to select which of a set of possible "linkable words" to link to the word currently being read.


Kuhlmann (2010) investigates several such classes, based on well-nestedness and gap degree constraints (Bodirsky, Kuhlmann, and Möhl 2005), relating them to lexicalized constituency grammar formalisms.

kuhlmann shows that linear context-free rewriting systems (LCFRS) with fan-out k (Vijay-Shanker, Weir, and Joshi 1987;Satta 1992) induce the set of dependency structures with gap degree at most k − 1; coupled CFG in which the maximal rank of a nonterminal is k (Hotz and Pitsch 1996) induces the set of wellnested dependency structures with gap degree at most k − 1; and finally, LTAG (Joshi and Schabes 1997) induces the set of well-nested dependency structures with gap degree at most 1.


Note that the examples with conflicting labels are not included in E en E cn .

if an example is in both E en and E cn , but the labels for the example are conflicting, the example will be excluded from E en E cn .


We adopt the sign test as a significance test because it is widely used in the field of text categorization (Yang and Liu 1999).

we use an on-line service 23 for performing sign tests in the experiments.


As can be seen in Tables 1 through 8, no matter which machine translation service is used, the proposed co-training approach (CoTrain) outperforms all baseline methods on the overall accuracy metric and most other metrics on the two test sets.

on the IT168 test set, the best accuracy is achieved by CoTrain with GoogleTranslate, and on the 360BUY test set, the best result is achieved by CoTrain with YahooTranslate.


We also find that the growth size (i.e., the numbers of positive and negative examples selected in the labeled data) is a very important factor in the proposed approach, which has great influence over the final performance.

a balanced growth leads to performance improvement, but an unbalanced growth hurts the final performance.


One of them is the practical use of semantic parses for concrete tasks or real-world applications.

the question "Now that we have such a nice shallow semantic representation, how do we use it for a concrete (e.g., commercial) task?"


One group tells us that "true" Bayesians do not adopt any model, they integrate over them.

if you don't know the correct model, you should plan accordingly and not commit.


It is simply a distribution with n "words," each one a single utterance.

it is a distribution that memorizes the training data.


By using a distance-based subgrouping method the Levenshtein is susceptible to exactly the same problems that classical lexicostatistics faced (Swadesh 1952).

there is a very high vulnerability to variation in retention rates (Bergsland and Vogt 1962;Blust 2000;Greenhill and Gray 2009).


Yet, in the prosodic transcriptions domain experts and non-experts achieved comparable levels of agreement, while the average agreement for mixed groups was significantly lower.

experts and novices were equally reliable, yet did not agree with each other.


Our data showed that experts and non-experts could achieve comparable levels of agreement, whereas the average agreement for mixed groups was significantly lower.


The important aspect to determine comparability is whether the coding problems and procedures that are typical for these three domains are also typical for linguistic annotations in general.

generalizability is given if coding problems and procedures found in the three domains in our analysis are typical for the linguistic domain our results are to be applied to.


1995 As usual, NP structure is different from the structure of other categories.

the Penn Treebank does not annotate the internal structure of noun phrases, instead leaving them flat.


4 This is not problematic, as the definition of what constitutes an NML or JJP node has not changed, only their representation in the corpus.

the dependencies that can be drawn from the NPs remain the same.


The first of these, Original structure, evaluates only the brackets that existed before the NP augmentation.

the NML and JJP brackets are removed before calculating these figures, in the same way that the NPB brackets added as part of Collins's parsing process are excised.


The next largest source of errors is mislabeling the bracket itself.

distinguishing between using NP and NML labels, as well as ADJP and JJP, accounts for 75 of the 92 errors.


The parser is not very good at replicating this pattern, perhaps because these constituents would usually not be bracketed if it were not for the possessive.

nML nodes that begin with a determiner are quite rare, only occurring when a possessive follows.


One potential approach to solving these problems would be to incorporate the information sources that were successfully applied to NP Bracketing into a Collins-style parser.

the possibility exists to include NER and Web 1T features as additional probability distributions in the model.


Despite this, our analysis of Collins's model highlighted a number of interesting points.

the continued importance of the base-NP submodel was a surprising result, as performance dropped spectacularly when it was removed.


In this article, we present a model for generating references to people in news summaries that incorporates insights from both theory and a corpus analysis of human written summaries.

our model captures how two properties of a person referred to in the summary-familiarity to the reader and global salience in the news story-affect the content and form of the initial reference to that person in a summary.


Our WEKA machine learner for the major/minor distinction achieved a testing accuracy of 74% on these 103 examples.

we can reproduce human judgment on which people to refer to by name in three quarters of cases.


Within the discourse, sentences are considered from right to left.

entities in more recent sentences are more salient than those in previous ones, and within a sentence, earlier references are more salient than later ones.


We find that for the news summary genre, the form and content of initial reference is critical, and our studies have thus focused on this.

due to the nature of our genre, we do not attempt to model anaphoric phenomena such as pronouns and common noun co-reference.


Consequently, in this article, we present the following contributions: r We demonstrate that class-based models can be made more effective.

we put forward the half-context hypothesis as a general principle on the basis of which to construct class-based language models.


The representation employed in this article builds on a specification used in our earlier work (Schütze 1993(Schütze , 1995Schütze and Walsh 2008), motivated by Exemplar Theory (Hintzman 1986;Nosofsky 1986;Pierrehumbert 2001), where rich exemplar representations facilitated the acquisition of local grammatical knowledge and outperformed a categorical representation in the same task.

each word was represented in terms of its immediate left and right neighborhood context.


The reason is that we use two distinct and unrelated representations, left-context distributions to induce classes of predictees and right-context distributions to induce classes of predictors.

half-context models are inherently asymmetric, reflecting the fact that language models are inherently asymmetric: The role of the predictor and the predicted are different.


The comparisons on lines 2 vs. 4 and 6 and 7 vs. 9 and 11 clearly show that the quality of bisecting k-means whole-context clustering is comparable to, if not better than, Brown-type whole-context clustering.

keeping the representation constant in both cases (i.e., whole-context) enables us to see the algorithmic benefits of bisecting k-means as it appears to offer more useful clusters than those produced by the exchange algorithm.


As for the order-3 models (Table 4 models 12-16), here the significance results demonstrate that half-contextualization yields statistically significant improvements over whole-context models.

p (Half) significantly outperforms p (Whole) , and p (KN-Half) significantly outperforms p (KN-Whole) .


In addition, although P (KN) demonstrates superior performance to P (Whole) and P (Half) , interpolation with either of our exemplartheoretic models yields significantly better performance over P (KN) alone.

both P (KN-Whole) and P (KN-Half) significantly improve on P (KN) .


In principle, the gain from finding an appropriate cluster for a rare word is as large as the gain from finding an appropriate cluster for a frequent word.

frequent idiosyncratic words have no advantage compared to infrequent idiosyncratic words and very frequent idiosyncratic words are less likely to be assigned to singleton clusters or small clusters dominated by them.


Sikkel's definition of parsing schemas (Sikkel 1997) extends deductive systems by formally defining the semantics of items and related concepts used in deductive systems.

items are sets of partial constituency trees that are licensed by rules of the grammar.


While there is more than enough content in this book, it does not cover the use of parsing schemas in machine translation.

formal properties of schemas might make it easier to describe and implement the integration of language models into parsing algorithms for synchronous context-free grammars (Chiang 2007).


Although broader studies focusing on the pragmatic function of cue phrases, discourse markers, linguistic feedback, and dialogue acts do shed light on the particular subset of utterances we are studying, and although there is some information on particular lexical items we include here in our study, the class of ACWs itself has received little attention.

given the frequency of ACWs in dialogue, it is important to identify reliable and automatically extractable cues to their disambiguation, so that spoken dialogue systems can recognize the pragmatic function of ACWs in user input and can produce ACWs that are less likely to be misinterpreted in system output.


Therefore, we downsampled the tokens of ACWs in the Games Corpus to obtain a balanced data set, with instances of each word and function coming in similar proportions from as many speakers as possible.

we downsampled our data using the following procedure: First, we discarded all word/function pairs with tokens from fewer than four different speakers; second, for each of the remaining word/function pairs, we discarded tokens (at random) from speakers who contributed more than 25% of its tokens.


Specifically, we downsampled our data using the following procedure: First, we discarded all word/function pairs with tokens from fewer than four different speakers; second, for each of the remaining word/function pairs, we discarded tokens (at random) from speakers who contributed more than 25% of its tokens.

the resulting data set meets two conditions: For each word/ function pair, (a) tokens come from at least four different speakers, and (b) no single speaker contributes more than 25% of the tokens.


In our case, by agreeing with what the speaker has said, the listener indicates that enough information has been provided and that any subsequent phrases may refer to a different topic.

such closure might mean that the proposition preceding the ACW has been added to the current context space (Reichman 1985), or that a new focus space is about to be created (Grosz and Sidner 1986).


Notably, Agr instances of mm-hm and uh-huh present a very different behavior from the other lexical items, with a distribution of final intonations that closely resembles that of backchannels.

over 60% of the Agr tokens of mm-hm and uh-huh are produced with final rising intonation (either L-H% or H-H%).


Although Figure 6 shows that Chk tokens of right tend to end in a steeply rising pitch, the RMANOVA tests yield between-subjects p-values of 0.01 or lower, indicating substantial speaker effects.

even though the general tendency for these tokens, as indicated by both the numeric and categorical variables, seems to be to end in a high-rising intonation, there is evidence of different behavior for some individual speakers, which keeps us from drawing general conclusions about this pragmatic function of right.


We argue that the mere association of words with vectors is not enough to constitute a theory of meaning-a theory of meaning must allow us to interpret these vectors.

it should be able to tell us whether one meaning entails or implies another; indeed this is one meaning of the verb to mean.


Considered as vector spaces, these are Banach spaces, because they are complete with respect to the associated norm, and under the component-wise lattice operations, they are Banach lattices.

the 1 space is an abstract Lebesgue space under the 1 norm.


This leads us to instead define multiplication on a subspace of L ∞ (A * × A * ).

the subspace A of L ∞ (A * × A * ) is the set defined by it is the space of all vectors formed from linear combinations of context vectors.


This has a basis consisting of elements e x for x ∈ A * , where e x the function that is 1 on x and 0 elsewhere.


By formalizing the notion of "meaning as context" we have been able to build a mathematical model that informs us about the nature of meaning under this paradigm.

it gives us a theory about how to represent words and phrases using vectors, and tells us that the product of two meanings should be distributive and associative.


However, regarding the choice of Scale, we can filter out some solutions on the assumption that speakers/writers apply a trade-off between roundness and accuracy.

we can show that however these two factors are weighted (provided that they are not judged irrelevant or even undesirable), some solutions will be inferior to others and can therefore be discarded.


We formalize the detection of similar sentences and the projection of role annotations in graph-theoretic terms by conceptualizing the similarity between labeled and unlabeled sentences as a graph alignment problem.

we represent sentences as dependency graphs and seek an optimal (structural) alignment between them.


We conceptualize the similarity between subgraphs representing predicate-argument structures as an alignment problem.

we seek to find an optimal alignment between the alignment domain M of a labeled graph and the alignment range N of an unlabeled sentence.


Our definition of the lexical similarity measure, lex, uses a vector space model of word co-occurrence which we created from a lemmatized version of the BNC.

we created a semantic space with a context window of five words on either side of the target word and the most common 2,000 context words as vector dimensions.


We optimized α on a development set using F 1 score as the objective function.

we used a random sample of 20% of the FrameNet instances as seed corpus and expanded it with instances from the BNC using different values for α.


We also compared our approach to self-training by selecting k sentences from the unlabeled corpus, labeling them with the baseline classifier trained on the unexpanded corpus (instead of applying our projection method), and then adding these to the training corpus and retraining the classifier.

we employed three variants of self-training.


These results indicate that the ability to improve labeling performance is not merely due to selecting sentences similar to the seeds.

the graph alignment algorithm is worth the added work as the projection of annotations contributes to achieving better SRL results.


To gain a better understanding of the quality of the annotations inferred by our system, we further analyzed a small sample.

we randomly selected 100 seed instances from the FrameNet corpus, and used 59,566 instances as the unlabeled expansion corpus, treating their gold standard annotations as unseen (the remaining 1,000 instances were held out as a separate test set, as discussed subsequently).


Unlike the neighbors used in our previous experiments, these are mutually exclusive.

the set for d = 1 includes only the first most similar neighbors, for d = 2 the second most similar neighbors, and so on.


To evaluate which of the methods just described performs best, we used a leave-one-out procedure over the FrameNet predicates marked as "known" in our experimental set-up.

we set aside one predicate at a time and use all remaining predicates to predict its frame candidates.